Biological Systems Engineering Lab

  • Biological Systems Engineering Lab
  • Research Groups
  • BIO-REMOTE
  • BIO-REMOTE
  • Biweekly lab seminar 1
  • Biweekly lab seminar 2
  • Biweekly lab seminar 3
  • Biweekly lab seminar 4
  • Biweekly lab seminar 5
  • Biological Systems Engineering Lab
  • Research Groups
  • BIO-REMOTE
  • BIO-REMOTE
  • Medical engineering group Seminar 1
  • Medical engineering group Seminar 2
  • Kansei brain group Seminar 1
  • Kansei brain group Seminar 2
  • Kansei brain group Seminar 3
  • Kansei brain group Seminar 4
  • Human modeling group Seminar 1
  • Human modeling group Seminar 2
  • Human modeling group Seminar 3
  • Human modeling group Seminar 4
  • Biological signal analysis group seminar 1
  • Biological signal analysis group seminar 2
  • Biological signal analysis group seminar 3
  • Biological signal analysis group seminar 4
  • Biological signal analysis group seminar 5
  • Biological signal analysis group seminar 6
  • Biological signal analysis group seminar 7
  • Biological Systems Engineering Lab
  • Research Groups
  • BIO-REMOTE
  • BIO-REMOTE
  • Biweekly lab meeting 4
  • Biweekly lab meeting 4
  • Biweekly lab meeting 4
  • Biweekly lab meeting 4
  • Biweekly lab meeting 4
  • Biweekly lab meeting 4
  • Biweekly lab meeting 4
  • Biological Systems Engineering Lab
  • Research Groups
  • BIO-REMOTE
  • BIO-REMOTE
  • Laboratory group photo 1
  • Laboratory group photo 2
  • Biological Signal Analysis Group
  • Medical engineering group
  • Human modeling group
  • Kansei brain group
  • top_image_hidden

Living organisms developed in nature through the evolution process are equipped with supremely skilled and sophisticated biological functions that cannot be realized with current engineering techniques. Analysis of these mechanisms may lead to not only elucidation of biological functions but also development of a wide variety of novel engineering systems.

From the viewpoint of a scientist approaching the secrets of living organisms and from that of an engineer developing machinery useful for human kind, the members of Biological Systems Engineering laboratory work on a wide variety of projects to analyze the characteristics of biological functions from theoretical and experimental approaches employing engineering techniques aiming to find new principles peculiar to biological systems, and develop novel medical/welfare apparatuses and industrial devices by applying the elucidated principles.

Through such research activities, the students can learn in-depth knowledge about biological systems based on electricity, electronics, systems and information engineering foundation allowing themselves to become creative engineers capable of seeking a new principle and expanding it into new fields.

Five research themes

There are still a lot of unknown functions and mechanisms hidden in the biological system. If we can elucidate and utilize them from engineering standpoint,then there is a possibility of creating new technologies to carve out the future of the 21st century. The Biological Systems Engineering Laboratory categorizes the broad research field of biological systems into five major research themes in order to explore specific research projects under each theme, and further functionally coordinate and fuse each theme to create novel research fields.

Biological signal analysis and its application to human interfaces

We develop novel signal processing algorithms that enablethe interpretation of human motions, intentions, and physiological/psychological states contained in biological signals, such as myoelectric signals, electroencephalograms, and electrocardiograms, as well as create robotic interfaces and medical welfare equipment.

Biomechanical analysis and its application to human-machine system design

We model human sensory/motor functions from electrical and electronicperspectives based on experimentally measured data, and develop novel movement support systems and next-generation automobile control systems by incorporating modeled human characteristics.

Statistical structure of neural networks based on novel machine learning algorithms

We propose new machine learning algorithms and neural networks based on probabilistic statistical theory and applythese to the development oflearning and control technologiesfor robots, medical welfare equipment, and medical data classificationtechnology.

Brain function/neural network modeling and artificial life models

Focusing on functions such as locomotion generation, sensation, perception, learning, and judgment, we model brain functions from an engineering viewpoint using artificial neural networks. Ultimately, we aim to model and analyze higher brain functions, especially social brain functions that understand the minds of others and live harmoniously, and Kansei that involves nonverbal, unconscious, and intuitive sensibilities. We also develop artificial life form models based on biological knowledge using the constructed brain models.

Biometric information mining technology and medical support systems

We are engaged in the research and development of novel medical support systems and medical devices through medicine-engineering collaborations by utilizing electric and electronic systems and information engineering technologies, such as biomechanical analysis technology, biological signal analysis technology, machine learning technology, and biological simulation technology that were developed in the Biological Systems Engineering laboratory.

Publications

Our research results have been published in scientific journals, books, conference proceedings, patent, etc.. The numbers of publications the lab produced are shown as follows
(as of October 3, 2022):

Latest papers

Artificially-reconstructed brain images with stroke lesions from non-imaging data: modeling in categorized patients based on lesion occurrence and sparsity

Stephanie Sutoko, Hirokazu Atsumori, Akiko Obata, Ayako Nishimura, Tsukasa Funane, Masashi Kiguchi, Akihiko Kandori, Koji Shimonaga, Seiji Hama, and Toshio Tsuji
Scientific Reports, volume 12, Article number: 10116, doi.org/10.1038/s41598-022-14249-z , Published online: 16 June 2022. (SCI, IF=4.380)
URL
PDF

Projection of damaged visual and language regions on low Trail Making Test Part-B performance in stroke patients

Ayako Nishimura, Stephanie Sutoko, Masashi Kiguchi, Hirokazu Atsumori, Akiko Obata, Tsukasa Funane, Akihiko Kandori, Tomohiko Mizuguchi, Koji Shimonaga, Seiji Hama, and Toshio Tsuji
Frontiers in Neurology, Volume 13, Article 853942, doi: 10.3389/fneur.2022.853942 , published: 02 June 20 (SCI, IF=4.003)
URL
PDF

Toward a Robust Estimation of Respiratory Rate using Cardiovascular Biomarkers: Robustness Analysis under Pain Stimulation

Ziqiang Xu, Toshiki Sakagawa, Akira Furui, Shumma Jomyo, Masanori Morita, Masamichi Ando, and Toshio Tsuji
IEEE Sensors Journal, Volume: 22, Issue: 10, pp. 9904-9913, Digital Object Identifier: 10.1109/JSEN.2022.3165880 , Publication Date: MAY 15, 2022 (SCI, IF=3.301)
URL

Beat-to-beat Estimation of Peripheral Arterial Stiffness from Local PWV for Quantitative Evaluation of Sympathetic Nervous System Activity

Ziqiang Xu, Toshiki Sakagawa, Akira Furui, Shumma Jomyo, Masanori Morita, Masamichi Ando, and Toshio Tsuji
IEEE Transactions on Biomedical Engineering, Digital Object Identifier: 10.1109/TBME.2022.3154398 , Date of publication: 25 February 2022 (SCI, IF=4.538)
URL

Increased cerebrovascular reactivity in selected brain regions after extracranial-intracranial bypass improves the speed and accuracy of visual cancellation in patients with severe steno-occlusive disease: A preliminary study

Koji Shimonaga, Seiji Hama, Akira Furui, Akiko Yanagawa, Akihiko Kandori, Hirokazu Atsumori, Shigeto Yamawaki, Toshinori Matsushige, and Toshio Tsuji
Neurosurgical Review, Published online: 30 January 2022  (SCI, IF=3.042)
URL
PDF

A Neural Network Based on the Johnson SU Translation System and Related Application to Electromyogram Classification

Hideaki Hayashi, Taro Shibanoki, and Toshio Tsuji
IEEE Access, Volume: 9, pp. 154304-154317, Digital Object Identifier: 10.1109/ACCESS.2021.3126348, Date of Publication: 08 November 2021 (SCI, IF=3.367)
URL
PDF

EMG Pattern Recognition via Bayesian Inference with Scale Mixture-Based Stochastic Generative Models

Akira Furui, Takuya Igaue, and Toshio Tsuji
Expert Systems with Applications, vol. 185, 115644, doi.org/10.1016/j.eswa.2021.115644, Available online 30 July 2021. (SCI, IF = 6.954)
URL
PDF

Forward and backward locomotion patterns in C. elegans generated by a connectome-based model simulation

Kazuma Sakamoto, Zu Soh, Michiyo Suzuki, Yuichi Iino, and Toshio Tsuji
Scientific Reports, volume 11, Article number: 13737, doi.org/10.1038/s41598-021-92690-2, Published online: 02 July 2021. (SCI, IF=4.379)
URL
PDF

The right hemisphere is important for driving-related cognitive function after stroke

Koji Shimonaga, Seiji Hama, Toshio Tsuji, Kazumasa Yoshimura, Shinya Nishino, Akiko Yanagawa, Zu Soh, Toshinori Matsushige, Tatsuya Mizoue, Keiichi Onoda, Hidehisa Yamashita, Shigeto Yamawaki, and Kaoru Kurisu
Neurosurgical Review, vol. 44, pp.977-985, doi.org/10.1007/s10143-020-01272-9, Published online: 11 March, 2020, 2021 (SCI, IF=2.654)
URL
PDF

Representative papers

Human Hand Impedance Characteristics during Maintained Posture in Multi-Joint Arm Movements

T. Tsuji, P. Morasso, K. Goto, and K. Ito
Biological Cybernetics, Vol.72, pp.475-485, 1995.

A Log-Linearized Gaussian Mixture Network and Its Application to EEG Pattern Classification

T. Tsuji, O. Fukuda, H. Ichinobe, and M. Kaneko
IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, Vol. 29, No. 1, pp. 60-72, February 1999.

A Recurrent Log-linearized Gaussian Mixture Network

T. Tsuji, N. Bu, M. Kaneko, and O. Fukuda
IEEE Transactions on Neural Networks, Vol.14, No.2, pp.304-316, March 2003.

A Human-Assisting Manipulator Teleoperated by EMG Signals and Arm Motions

O. Fukuda, T. Tsuji, M. Kaneko and A. Otsuka
IEEE Transactions on Robotics and Automation, Vol.19, No.2, pp.210-222, April 2003.

Quantitative Evaluation of Pain during Electrocutaneous Stimulation using a Log-Linearized Peripheral Arterial Viscoelastic Model

H. Matsubara, H. Hirano, H. Hirano, Z. Soh, R. Nakamura, N. Saeki, M. Kawamoto, M. Yoshizumi, A. Yoshino, T. Sasaoka, S. Yamawaki, and T. Tsuji
Scientific Reports, volume 8, Article number: 3091, doi:10.1038/s41598-018-21223-11, Published online: 15 February 2018.

Continuous Blood Viscosity Monitoring System for Cardiopulmonary Bypass Applications

S. Okahara, Z. Soh, S. Miyamoto, H. Takahashi, S. Takahashi, T. Sueda, and T. Tsuji
IEEE Transactions on Biomedical Engineering, Vol.64, No.7, pp. 1503-1512, DOI:10.1109/TBME.2016.2610968, JULY 2017.

Assessment of Lower-limb Vascular Endothelial Function Based on Enclosed Zone Flow-mediated Dilation

H. Hirano, R. Takama, R. Matsumoto, H. Tanaka, H. Hirano, Z. Soh, T. Ukawa, T. Takayanagi, H. Morimoto, R. Nakamura, N. Saeki, H. Hashimoto, S. Matsui, S. Kishimoto, N. Oda, M. Kajikawa, T. Maruhashi, M. Kawamoto, M. Yoshizumi, Y. Higashi, and T. Tsuji
Scientific Reports, volume 8, Article number: 9263, doi:10.1038/s41598-018-27392-3, Published online: 18 June 2018.

A computational model of internal representations of chemical gradients in environments for chemotaxis of Caenorhabditis elegans

Z. Soh, K. Sakamoto , M. Suzuki , Y. Iino, and T. Tsuji
Scientific Reports, volume 8, Article number: 17190, doi:10.1038/s41598-018-35157-1, Published online: 21 November 2018.

A Scale Mixture-based Stochastic Model of Surface EMG Signals with Variable Variances

A. Furui, H. Hayashi, and T. Tsuji
IEEE Transactions on Biomedical Engineering, DOI: 10.1109/TBME.2019.2895683, Date of Publication: 28 January 2019.

A myoelectric prosthetic hand with muscle synergy-based motion determination and impedance model-based biomimetic control

A. Furui, S. Eto, K. Nakagaki, K. Shimada, G. Nakamura, A. Masuda, T. Chin, and T. Tsuji
Science Robotics, Vol. 4, Issue 31, eaaw6339, DOI: 10.1126/scirobotics.eaaw6339, 26 June 2019.

Markerless Measurement and Evaluation of General Movements in Infants

T. Tsuji, S. Nakashima, H. Hayashi, Z. Soh, A. Furui, T. Shibanoki, K. Shima, and K. Shimatani
Scientific Reports, volume 10, Article number: 1422, doi:10.1038/s41598-020-57580-z, Published online: 29 January 2020.